Mohanasankar Sivaprakasam

The vascular endothelium, once considered an inert barrier, is now recognized as a dynamic, multifunctional organ central to cardiovascular homeostasis. It regulates vascular tone, blood pressure, coagulation, inflammation, and angiogenesis through a repertoire of biochemical mediators and mechanosensitive responses. Endothelial dysfunction (ED), a state of impaired vasodilatory, anti-inflammatory, and anti-thrombotic capacity, is a critical early event in the pathogenesis of cardiovascular, metabolic, and inflammatory diseases. This chapter reviews the structural and molecular foundations of endothelial biology, its physiological integration into cardiovascular function, and the pathophysiological basis of ED. We critically evaluate biochemical, invasive, and non-invasive approaches for endothelial function quantification, highlighting methodological challenges and the urgent need for standardization. Finally, we present recent advances, including novel local pulse wave velocity-based endothelial reactivity assessment techniques, and discuss future prospects for clinical translation, precision medicine, and therapeutic monitoring.

Automated cardiac interpretation in resource-constrained settings (RCS) is often hindered by poor-quality echocardiographic imaging, limiting the effectiveness of downstream diagnostic models. While super-resolution (SR) techniques have shown promise in enhancing magnetic resonance imaging (MRI) and computed tomography (CT) scans, their application to echocardiography-a widely accessible but noise-prone modality-remains underexplored. In this work, we investigate the potential of deep learning-based SR to improve classification accuracy on low-quality 2D echocardiograms. Using the publicly available CAMUS dataset, we stratify samples by image quality and evaluate two clinically relevant tasks of varying complexity: a relatively simple Two-Chamber vs. Four-Chamber (2CH vs. 4CH) view classification and a more complex End-Diastole vs. End-Systole (ED vs. ES) phase classification. We apply two widely used SR models-Super-Resolution Generative Adversarial Network (SRGAN) and Super-Resolution Residual Network (SRResNet), to enhance poor-quality images and observe significant gains in performance metric-particularly with SRResNet, which also offers computational efficiency. Our findings demonstrate that SR can effectively recover diagnostic value in degraded echo scans, making it a viable tool for AI-assisted care in RCS, achieving more with less.

Heart transplantation remains the definitive treatment for advanced heart failure. Despite over five decades of advancements in transplantation, the fundamental approach to organ preservation outside the body has remained largely unchanged. Traditionally, the field has relied on static cold storage (SCS) as the clinical standard for preserving hearts from donors after brainstem death. While effective, SCS presents practical limitations, particularly in mitigating ischemic damage, assessing the organ quality in storage and transport, and expanding the donor pool to include extended-criteria donors or donations after circulatory death (DCD). The emergence of ex-vivo machine perfusion, a transformative innovation, offers enhanced preservation by reducing the risk of ischemic damage alongside the ability to assess and optimize grafts from extended-criteria donors and those obtained through DCD. This chapter explores the pivotal role of cardiac biomarkers in graft evaluation within this emerging technology, categorized into functional markers, cell injury and cell death markers, inflammatory markers, omics-based markers, and hormone/pro-hormone markers. By enhancing precision in identifying transplantable hearts, these biomarkers play a crucial role in improving the safety and efficacy of marginal graft utilization, providing reassurance about the advancements in the field. The chapter also includes practical recommendations for integrating these biomarkers into clinical protocols and outlines future research directions to drive innovation in cardiac transplantation.

The updated recommendations on diagnostic procedures and treatment pathways for a medical condition are documented as graphical flows in Clinical Practice Guidelines (CPGs). For effective use of the CPGs in helping medical professionals in the treatment decision process, it is necessary to fully capture the guideline knowledge, particularly the contexts and their relationships in the graph. While several existing works have utilized these guidelines to create rule bases for Clinical Decision Support Systems, limited work has been done toward directly capturing the full medical knowledge contained in CPGs. This work proposes an approach to create a contextually enriched, faithful digital representation of National Comprehensive Cancer Network (NCCN) Cancer CPGs in the form of graphs using automated extraction and node & relationship classification. We also implement semantic enrichment of the model by using Large Language Models (LLMs) for node classification, achieving an accuracy of 80.86% and 88.47% with zero-shot learning and few-shot learning, respectively. Additionally, we introduce a methodology for answering natural language questions with constraints to guideline text by leveraging LLMs to extract the relevant subgraph from the guideline knowledge base. By generating natural language answers based on subgraph paths and semantic information, we mitigate the risk of incorrect answers and hallucination associated with LLMs, ensuring factual accuracy in medical domain Question Answering.

The human brain is believed to contain a full complement of neurons by the time of birth together with a substantial amount of the connectivity architecture, even though a significant amount of growth occurs postnatally. The developmental process leading to this outcome is not well understood in humans in comparison with model organisms. Previous magnetic resonance imaging (MRI) studies give three-dimensional coverage but not cellular resolution. In contrast, sparsely sampled histological or spatial omics analyses have provided cellular resolution but not dense whole brain coverage. To address the unmet need to provide a quantitative spatiotemporal map of developing human brain at cellular resolution, we leveraged tape-transfer assisted serial section histology to obtain contiguous histological series and unbiased imaging with dense coverage. Interleaved 20μ thick Nissl and H&E series and MRI volumes are co-registered into multimodal reference volumes with 60μ isotropic resolution, together with atlas annotations and a stereotactic coordinate system based on skull landmarks. The histological atlas volumes have significantly more contrast and texture than the MRI volumes. We computationally detect cells brain-wide to obtain quantitative characterization of the cytoarchitecture of the developing brain at 13-14 and 20-21 gestational weeks, providing the first comprehensive regional cell counts and characterizing the differential growth of the different brain compartments. Morphological characteristics permit segmentation of cell types from histology. We detected and quantified brain-wide distribution of mitotic figures representing dividing cells, providing an unprecedented spatiotemporal atlas of proliferative dynamics in the developing human brain. Further, we characterized the abundance and distribution of Cajal-Retzius cells, a transient cell population that plays essential roles in organizing glutamatergic cortical neurons into layers. Together, our study provides an unprecedented quantitative window into the developing human brain and the reference volumes and coordinate space should be useful for integrating spatial omics data sets with dense histological context.

Background: Wave separation analysis (WSA) performed at the common carotid artery (CCA) reveals insights into cerebral vascular pathophysiology; however, the conventional WSA (WSAREF) necessitates the availability of both pressure and flow waveforms measured from the same arterial site. Objective: We propose a method for performing WSA at CCA using a single pulse waveform (WSAm-RAY), by modelling the shape of the CCA flow using multi-Rayleigh curves. The WSAm-RAY reduces the measurement complexity, by modelling the flow waveform shape, targeted at scenarios with limited resources, where comprehensive equipment, specialized personnel and hospital settings are often lacking. The modelled flow was compared with the measured flow for accuracy in modelling the flow morphology. The performance of WSAm-RAY was evaluated by comparing the reflection quantification indices derived from WSAREF and WSAm-RAY. Methods: WSAm-RAY employs weighted and shifted multi-Rayleigh functions, time-optimized for characteristic flow peaks in the early and late systolic phase of the CCA flow waveform. The reliability of the modelled flow and performance of WSAm-RAY were validated on 70 (28 female) participants (age: 20 to 51 years). Continuous recording of CCA flow velocity and diameter waveforms were recorded from the participants. Results: The root-mean-squared-error in forward and backward pressure waves was 2.18 ± 0.97 mmHg (∼ 2.5% of the average mean arterial pressure of the study participants). A statistically significant and strong correlation (r > 0.76, p < 0.001) was observed among reflection quantification indices from WSAREF and WSAm-RAY. Conclusion: WSAm-RAY potentially expands the scope of vascular screenings using a single pulse measurement targeting resource constrained settings.